Haiwen (Haven) Feng

Haven (Haiwen) Feng

Hi, I'm Haven! I'm a 3rd year PhD student at Max Planck Institute for Intelligent Systems (MPI-IS), advised by Michael J. Black.
Starting in Autumn 2024, I will be visiting Berkeley AI Research , where I will be advised by Angjoo Kanazawa. I've also spent a wonderful summer with Marc Levoy's group at Adobe in 2023, hosted by Cecilia Zhang.

Before my PhD, I received M.Sc. and B.Sc. degrees in Physics at the University of Tübingen. For my master's, I researched disentangled representation learning with Wieland Brendel and Matthias Bethge at Tübingen AI Center. During my bachelor's, I worked on 3D face reconstruction with Timo Bolkart and Michael J. Black at MPI-IS.
I was born and raised in Beijing.

Email / Scholar / Twitter / Linkedin / Github

Research

I'm interested in machine learning, computer vision, computer graphics, and particularly in how to understand and recreate our physical world in a principled and generalized way. My research centers on visual inverse problems and controllable generation with foundation models (various kinds of them). Selected publications are listed below (* indicates equal contribution).

	SGP-Bench: Can Large Language Models Understand Symbolic Graphics Programs? Zeju Qiu, Weiyang Liu, Haiwen Feng, Zhen Liu, Tim Z. Xiao, Katherine M. Collins, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf Preprint*, 2024 project page / arXiv / code We questioned whether LLMs can "imagine" how the corresponding graphics content would look without visually seeing it! This task requires both low-level skills (e.g., counting objects, identifying colors) and high-level reasoning (e.g., interpreting affordances, understanding semantics). Our benchmark effectively differentiates models by their reasoning abilities, with performance consistently aligning with the scaling law!
	IG-LLM: Re-Thinking Inverse Graphics With Large Language Models Peter Kulits, Haiwen Feng, Weiyang Liu, Victoria Abrevaya, Michael J. Black TMLR, 2024 project page / arXiv We explored if inverse graphics could be approached as a code generation task and found it generalize surprisingly well to OOD cases! However, is it optimal for graphics? Our research identifies a fundamental limitation of LLMs for parameter estimation and offers a simple but effective solution.
	Explorative Inbetweening of Time and Space Haiwen Feng, Zheng Ding, Zhihao Xia, Simon Niklaus, Victoria Abrevaya, Michael J. Black, Xuaner Zhang ECCV, 2024 project page / arXiv We proposed "Time-Reversal Fusion" to enable the image-to-video model to generate towards a given end frame without any tuning. It not only provides a unified solution for three visual tasks but also probes the dynamic generation capability of the video diffusion model.
	OFT: Controlling Text-to-Image Diffusion by Orthogonal Finetuning Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, Bernhard Schoelkopf NeurIPS, 2023 project page / arXiv / code We proposed a principled PEFT method by orthogonally fine-tuning the pretrained model, resulting in superior alignment and faster convergence for controllable synthesis.
	ArtEq: Generalizing Neural Human Fitting to Unseen Poses With Articulated SE(3) Equivariance Haiwen Feng, Peter Kulits, Shichen Liu, Michael J. Black, Victoria Abrevaya ICCV, 2023 (Oral Presentation) project page / arXiv / code We extended SE(3) Equivariance to articulated scenarios, achieving principled generalization for OOD body poses with 60% less error, and a network 1000 times faster and only 2.7% the size of the previous state-of-the-art model.
	TRUST: Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation Haiwen Feng, Timo Bolkart, Joachim Tesch, Michael J. Black, Victoria Abrevaya ECCV, 2022 project page / arXiv / code We conducted a systematic analysis of skin tone bias in 3D face albedo reconstruction and proposed the first unbiased albedo estimation evaluation suite (benchmark + metric). Additionally, we developed a principled method that reduces this bias by 80%.
	DECA: Learning an Animatable Detailed 3D Face Model from In-The-Wild Images Yao Feng, Haiwen Feng, Michael J. Black, Timo Bolkart SIGGRAPH, 2021 project page / arXiv / code We built the first animatable facial detail model that is purely learned from in-the-wild images and generalize to new expressions.

The template is stole from Jon Barron.